Automated mineral mapping in optical ore microscopy
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1 Automated mineral mapping in optical ore microscopy Accuracy and limitations Eric PIRARD Université de Liège GeMMe - Georesources & GeoImaging
2 Automated Mineral Mapping Sampling Imaging Mineral Identification
3 Automated Mineral Mapping Imaging Spatial Sampling A random point sampling gives an unbiased estimator of the volume proportion of a phase (α) in a solid 1 st Principle of Stereology (Delesse,1848) P = A = V α P α A α V Systematic sampling on a random section Do more less well
4 Automated Mineral Mapping Sensing the mineral target Sensing principle» Ex. X-ray fluorescence; light absorption; atomic force; Signal processing and interpretation» Database of spectra; Characteristic rays (K α, ) Goethite
5 Automated Mineral Mapping Whiskbroom imaging mode Scanning beam or moving sample Ex. Scanning Electron Microscopy Result of EDX mapping QEM SCAN
6 Automated Mineral Mapping Pushbroom imaging mode Scanning linear sensor Diffuse reflectance imaging Industrial vision of «marble» tiles Specular reflectance imaging
7 Automated Mineral Mapping Array imaging mode CCD/CMOS camera Reflected Light Microscopy Typical Quantum Efficiency for an Si-detector
8 Photonic Ore Microscopy Photons Filters Reflectance database
9 Photonic Ore Microscopy MultiSpectral Imaging Conventional Ore Microscope» Objective transmittance 1100nm Scientific grade CCD camera» Spectral sensitivity 350nm-1000nm Filter wheel» Interference 50 nm spacing 360 nm 438 nm 489 nm 591 nm 692 nm 870 nm Typical Quantum Efficiency Curve for an Si-detector Multispectral Image
10 Photonic Ore Microscopy MultiSpectral Imaging Calibration» Correction for uneven illumination» Measure of reflectance standard Correlation with Specular Reflectance Database» Quantitative Data File - QDFIII (Criddle & Stanley, 1993)» Extension to 1000 nm (Brea et al., IMA, 2010) BORNITE COVELITE PYRITE CHALCOCITE COVELITE PYRITE 55,00 CR , ,00 Reflectance (% x100) R Reflectance (%) ce (% ) Wavelength Wavelength (nm) Wavelength (nm) BN tfpe9 001_1 COV Ro (116) BN tfpe9 001_2 COV Re (116) BN tfpe9 001_3 Covelite Pyrite BN tfpe9 001_4 R tfpe9 BORNITE 011 QDFIII - Pyrite tfpe Chalcocite Chalcocite COM Reflectance (%) 40,00 35,00 30,00 25,00 20,00 15, Wavelength (nm) PO (CR286_012) PO (CR286_014) PO (CR286_020) CUB - (CR286_019) CUB - (CR286_019) CP (CR286_001) CP (CR286_007) Enhanced Pyrrhotite-Cubanite discrimination
11 Photonic Ore Microscopy MultiRadial Imaging Rotating incident polarizer» Information about crystal anisotropy Multiradial Image θ θ { P } min{ P } B = Max x, y x, y Bireflectance Image
12 Applications Epithermal Cu-Au Stratiform Cu Carbonate rocks
13 Wavelength Selection Phalaborwa : Cu-Ni 437nm; 489nm; 591nm; 692nm (10nm FWHM) Criddle & Stanley QDFIII, 1993 Pentlandite Chalcopyrite Violarite Pyrrhotite Cubanite Chalcocite Covellite Bornite Magnetite Califice A., 2008
14 Post Processing Chelopech : Epithermal Cu-Au Paragenesis False Colour Image Maximum Likelihood Classification Pyrite Resin After Conditional Geodesic Propagation Evrard M., 2012
15 Modal Analysis Kansanshi : Stratiform Cu Supergene (mixed) zone» Secondary Cu sulphides,» Malachite, 489nm, 590nm, 690nm Chalcopyrite (A) Chalcopyrite (B) Copper Cuprite Digenite Malachite Chalcocite Rutile Molybdenite Pyrite Siebels K., 2012
16 Modal Analysis Kansanshi : Stratiform Cu Supergene (mixed) zone» Secondary Cu sulphides,» Malachite, Mal Py Cp 54,2% Ss-Cu 11,1% Ox-Cu 3,0% Hem 1,7% S-Fe 25,1% Cc Cp1 Other 0,3% Gg 4.6% Cp2 Curvas de Liberación Ccp RoCo % Ccp Liberation curve in a flotation concentrate >99 % Liberación Lib Vol Ccp MIXTAS Lib Sup Ccp MIXTAS Lib Vol Ccp TOTAL Lib Sup Ccp TOTAL Dufrasne Fl., 2010; Perez-Barnuevo L., 2011
17 Microstructure Analysis Carbonate Rock Porosity Multiradial Image Jaimes Contreras R., 2011 Grain Boundaries (gradient)
18 Microstructure Analysis Carbonate Rock Longueur (µm) P90 P50 P10 Percentiles of Intercept Length (Crystal Size Distribution)
19 Accuracy & Limitations Visual check Chemistry, XRD Comparison with SEM Round Robin
20 Accuracy & Limitations Validation Visual» Point counting; Time Consuming; Subjective; Chemistry» Balance; Limited mineralogy; XRD» Major minerals; Round Robin Test» Interlaboratory test on «similar samples»» Detailed statistics of inter/intra variability» Initiative of IMA-CAM
21 Accuracy & Limitations Comparison with Automated EM-based Mineralogy QEM-SCAN/MLA High resolution (PGE, Au) Non-stoechiometric minerals (Lcx) Trace / Precious elements partitioning Gangue mineralogy Process mineralogy oriented software Optics Cheap technology Fast imaging Large samples (do more less well!) Good discrimination in some critical ores» Iron oxide; Ni-Cu sulfides; Multiradial imaging» Grain size analysis» Crystal orientation (EBSD for the poorest)
22 EMERALD Erasmus Mundus European Master Degree in Georesources Engineering Innovative education in «Geometallurgy»» Mineral Resources Characterization Processing Modelling - Management Worldwide network of associated universities» Moscow, Queensland, Capetown, Hacceteppe, Minas Gerais, UChile, Kazakhstan, Supported by major mining companies
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